Sound‐guided assessment and localization of pulmonary air leak

Abstract Pulmonary air leak is the most common complication of lung surgery, with air leaks that persist longer than 5 days representing a major source of post‐surgery morbidity. Clinical management of air leaks is challenging due to limited methods to precisely locate and assess leaks. Here, we present a sound‐guided methodology that enables rapid quantitative assessment and precise localization of air leaks by analyzing the distinct sounds generated as the air escapes through defective lung tissue. Air leaks often present after lung surgery due to loss of tissue integrity at or near a staple line. Accordingly, we investigated air leak sounds from a focal pleural defect in a rat model and from a staple line failure in a clinically relevant swine model to demonstrate the high sensitivity and translational potential of this approach. In rat and swine models of free‐flowing air leak under positive pressure ventilation with intrapleural microphone 1 cm from the lung surface, we identified that: (a) pulmonary air leaks generate sounds that contain distinct harmonic series, (b) acoustic characteristics of air leak sounds can be used to classify leak severity, and (c) precise location of the air leak can be determined with high resolution (within 1 cm) by mapping the sound loudness level across the lung surface. Our findings suggest that sound‐guided assessment and localization of pulmonary air leaks could serve as a diagnostic tool to inform air leak detection and treatment strategies during video‐assisted thoracoscopic surgery (VATS) or thoracotomy procedures.

due to faulty staple-lines through compromised tissue. [1][2][3][4][5][6] Pulmonary air leaks range in severity from "mild leaks" that often resolve spontaneously, to more serious "prolonged leaks" that require up to weeks to heal and often necessitate additional interventions. [7][8][9] Prolonged air leaks that persist beyond 5 days substantially increase the risk of complications, such as empyema and pneumonia, and are associated with increased hospital length of stay and cost. 8 Patients with severe air leaks develop pneumothorax, which can rapidly lead to tension pneumothorax, cardiovascular collapse, and death ( Figure S1). 10 Among the diverse population of lung surgery patients, most have underlying lung disease include chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), and lung cancer, which predisposes them to severe, prolonged air leaks. In these patients, to quantify and localize air leaks or assess prognosis remains challenging.
Methods to objectively assess severity and precisely locate pulmonary air leaks would enable targeted intervention and treatment.
For centuries, clinicians have relied on qualitative auscultation of lung sounds generated by airflow within the respiratory tract to diagnose respiratory conditions. 11,12 The properties of a periodic acoustic wave (e.g., frequency, amplitude) determine loudness, spectral density, and pitch. Waves with frequency between 20 Hz and 20 kHz are perceived as audible sounds. 13,14 As fluid passes by a structure, energy is transferred from the fluid to the structure, causing vibration that produces sound with intensities and frequencies dependent on the amplitudes and modes of vibration. 15 For example, the primary mechanism of human voice production is oscillation of the vocal cords produced by airflow through the glottis (fundamental frequency range: 110-300 Hz). 16,17 Significantly, we discovered that airflow through defective visceral pleura, that is, pulmonary air leak, also causes oscillations in the surrounding lung tissue, resulting in distinct, quantifiable air leak sounds ( Figure S2). This discovery led us to hypothesize that acoustic signatures of these sounds provide quantifiable information about the air leak, such as leak severity and location.
Clinical methods used to detect and assess pulmonary air leak remain limited, and do not precisely assess leak severity or locate leak site. We hypothesized that quantitative analysis of air leak sounds could enable detection, severity assessment, and precise localization of pulmonary air leak. To test this hypothesis, we developed a sound analysis system and methodology to evaluate pulmonary air leak in situ ( Figure 1 and Figure S3). In this study, we show that pulmonary air leaks can be evaluated by analyzing air leak sound signals. Further, we demonstrate that air leak sound profiles can be used to classify severity and localize the air leak with significantly higher resolution than any currently available methods. Using a clinically relevant large animal model (swine) of free-flowing air leak in an open thoracotomy, we evaluated post-surgical air leaks due to staple-line failure, and applied our quantitative soundguided methodology to distinguish mild and severe air leaks and to precisely localize air leak sites. We identified that air leak sounds contain frequencies that comprise harmonic series, a finding not previously reported. Collectively, our results indicate that pulmonary air leak sounds are quantifiable in a clinically relevant setting, and could be used for real-time severity assessment and precise localization of air leaks.

| Rat model of pulmonary air leak
Post-surgical air leak is caused by a perforation in the visceral pleura that often occurs along or near a staple line due to failure of the F I G U R E 1 Assessment and localization of pulmonary air leak using sound analysis. (a) Schematic of pulmonary air leak with computerassisted sound analysis system. Mic: microphone. F: frequency (b) Process flow diagram of air leak sound analysis for severity prediction and localization to improve clinical decision-making for patients with air leak staples or tearing of lung tissue. 4,5 Defects can occur (a) focally at a single staple or (b) along a series of consecutive staples, and range in size from <1 mm to >1 cm. In this study, we investigated small defects resulting from focal puncture (i.e., <1 mm) in a rat model of pulmonary air leak to assess the sensitivity of our system to detect minor leaks that result from failure of a single staple ( Figure 2). Rat lungs provide a suitable model to test sensitivity of our methodology and system because air pressures and tidal volumes of rat lungs are significantly lower than those of human lungs, corresponding to lower pressure drop (ΔP) across the lung alveoli and thoracic space, resulting in quieter air leak sounds. By validating efficacy of the sound analysis in rat lungs, we demonstrated high sensitivity and accuracy of our system and methodology. Accordingly, to induce air leak in rat lung, we punctured the visceral pleura of intubated rats that were ventilated with a tidal volume of 6 ml/kg at a ventilation frequency of 70 breaths per minute (bpm) using an 18-gauge (diameter: 1.27 mm) or 16-gauge (diameter: 1.65 mm) needle (Figure 2b and Video S1). Airway pressures were continuously monitored using a small animal ventilator, pressure sensors, and custom-written MATLAB scripts (Figure 2a and Figure S4a). Air leaks were confirmed by observation of decreased air pressure and compliance, as indicated by flattened peaks of the pressure curves. Focal puncture wound induced with 18-gauge and 16-gauge needle led to declines in peak inspiratory pressure of 39% (from 16.2 to 9.9 cmH 2 O; Figure S4c,d) and 48%, respectively (from 16.2 to 6.7 cmH 2 O; Figure S4e,f).

| Detection of pulmonary air leak in rat using acoustic evaluation of air leak sounds
We investigated whether changes in peak inspiratory pressure resulting from air leak would be accompanied by corresponding  Figure S5). Pressure difference (ΔP) between inside (P alveolar ) and outside (P Amb ) the lung is the major driving force that causes air leak. Due to difficulties in accurate measurement of P alveolar , we instead measured P airway .
While the two values were related, they were not the same. During normal ventilation, there is a pressure drop across the airways, dependent on driving frequency and airway structure. Accordingly, the loudest (47.5 dBA) and quietest (39.8 dBA) acquired sounds corresponded to the maximum (10.7 cmH 2 O) and minimum airway pressure (2.1 cmH 2 O), respectively, in each respiratory cycle. Loudness of the air leak sounds increased more rapidly during inspiration (24.84 dBA/s) than expiration (14.53 dBA/s), which suggested that air leak loudness depended on ventilation parameters (i.e., P airway , tidal volume, respiratory rate) which contributed to the total volume of air loss through the pleural defect.
To quantify air leak sound signatures, we analyzed the frequency distribution of the air leak sounds acquired from the rat model of air leak through spectral analysis (Figure 2g-j). Sound spectrograms were generated to assess time-varying intensity and frequency throughout the respiratory cycle. The spectrograms revealed that the air leak sounds contained a group of sound bands, each with a distinct frequency that increased and then decreased in patterns similar to those of P airway , amplitude, and sound loudness ( Figure 2h). This suggested that both loudness and frequency of air leak sounds could be used to characterize and quantify air leak. We then extracted the air leak sound near the plateau of the curves in the spectrogram (Figure 2h, dotted region in Figure S6a) and obtained power spectra to compare relative intensity of the frequency bands ( Figure 2i and Figure S6b).
We plotted the regions of air leak sound spectrograms that contained frequency bands, which consistently corresponded to inspiratory peaks. Seven distinct frequency bands (fb1-fb7) with amplitude above À80 dB were identified between 0 and 10 kHz, each with narrow and concentrated frequencies. The lowest frequency band (i.e., fb1: 890 Hz) had maximum intensity among the seven bands, and was determined to be noise generated by the ventilator. While intensity of the sound bands generally decreased with frequency, the band that centered at 5020 Hz (fb5) had the second strongest intensity. A harmonic sound consists of a series of sound frequencies at integer multiples of the fundamental frequency. 18 The ratios of fb3-fb7 (frequency range: 2524-8845 Hz) to fb2 (frequency: 1223 Hz) were approximately 2, 3, 4, 5, and 7, respectively, which indicated that these sound bands were a harmonic series. To determine the relationship between band frequency and sound intensity, we performed a linear regression on the identified frequency bands generated by air leak in rat, and found a statistically significant inverse correlation ( Figure 2j, Y = À0.00231X -49.1, where X is frequency and Y is frequency band intensity, R 2 = 0.702, p < 0.001).

| Swine model of pulmonary air leak
Swine are often used as a model for lung injury because the anatomy, size, and respiratory parameters are similar to those of human lungs. 19 We assessed if similar air leak sounds occurred in human-sized lungs and determined that the harmonic characteristic is a distinct, quantifi-  (Figure 3h). While the frequency bands extended beyond 8 kHz in rat lungs (Figure 2h and Figure S6), the bands predominantly remained below 4 kHz in swine lungs, possibly due to difference in defect size (20 mm in swine vs 1.3 mm in rat), as oscillation frequency by airflow is inversely proportional to defect length. 16,17 Frequency bands with amplitude above À60 dB identified at the plateau (i.e., fb1-4) were extracted and plotted in a power spectrum ( Figure S9a), in which sound intensity generally decreased with frequency, as in rat air leaks ( Figure S6). The fundamental frequency band (fb1: 665 Hz) generated the highest intensity sound. The ratios of fb2-4 (frequency range: 1355-2731 Hz) to fb1 were approximately 2, 3, and 4, respectively, comprising a harmonic series ( Figure S9b). We performed a linear regression on the identified frequency bands generated by air leak in swine, and found a statistically significant inverse correlation between band frequency and sound intensity ( Figure S9c, Y = À0.00792X -34.8, where X is frequency and Y is sound band intensity, R 2 = 0.896, p < 0.001).

| Assessment of air leak severity in swine lung using acoustic analysis
Air leak severity is typically described by the volume of air escaping the lung from the leak site. 20 To assess air leak severity using our sound-guided method, we modulated volume loss and analyzed the effects on air leak sounds. For a mild leak (air volume loss of 8 ml/breath), we detected multiple frequency bands of reduced sound intensities compared to those detected in severe air leak (Figure 3i,j and Videos S6 and Video S7). Transient high frequency spectral bands were found in the absence of air leak, as normal breath sounds display energy in broad bands depending on air flow, chest wall thickness, and location of sound acquisition ( Figure S10a and Video S8). On the other hand, in both mild (volume loss: 8 ml/breath; Figure S10b) and moderate (volume loss: 46 ml/breath; Figure S10c) air leaks, discernible frequency bands were observed on spectral analysis. We also noticed that sounds generated due to bursting of air bubbles near air leak site were visualized as multiple vertical lines in the spectrogram ( Figure S10c). Further, we assessed the sensitivity of our soundguided method in distinguishing subtle differences of air volume loss.
Results showed that our method can accurately differentiate two different levels of air volume loss representative of mild air leak by comparison of sound intensity (52.3 dBA for 8 ml/breath and 62.1 dBA for 15 ml/breath; Figure 3k). Collectively, the results suggest that sound-guided detection and quantification of pulmonary air leak can enable rapid and accurate assessment and differentiation of air leak severity with a high degree of sensitivity.

| Precise localization of pulmonary air leak via sound intensity measurements
Clinical methods, such as X-ray and ultrasound, detect the presence of pneumothorax resulting from air leak, and can only determine if the leak is in the left or right lung. Precise identification of the exact air leak site could facilitate targeted intervention and thus improve patient outcomes. Therefore, we investigated whether quantitative sound analysis could enable high-resolution localization of air leak site ( Figure 4). Rat lungs with focal puncture air leak were continuously ventilated while a breathing sound intensity matrix was constructed.
To generate breathing sound intensity matrix, we systematically measured the loudness of air leak sounds across a matrix with step size 1 cm. Starting from the upper left region of the lung, we recorded sound for 5 seconds and calculated average loudness. The microphone was then advanced to an adjacent square (e.g., 1 cm to the right), and sound acquisition and loudness quantification were repeated to obtain a heatmap showing distribution of the normalized sound intensity, ranging between 0 (no sound) and 1.00 (maximum sound), across the whole lung. To establish the baseline breathing sound intensity matrix, analysis was first performed in uninjured ventilated lungs ( Figure S11a,b), where all sound intensity scores ranged 0.19-0.20. Sound intensity analysis was repeated in lungs punctured with an 18-gauge needle. In the breathing sound intensity matrix of lungs with air leak, sound intensity strongly correlated with spatial proximity to the air leak site. Specifically, sound intensity in lungs with air leak ranged 0.18-1.00, where intensity of 1.00 corresponded to the precise location of air leak. This method consistently resulted in precise localization of air leak sites within 1 cm (Figure 4a,b).
We similarly assessed the ability of our system to locate the site of air leak using sound intensity measurement in human-sized swine lungs. We performed right middle lobe resection and measured sound loudness at 12 points prior to induction of air leak. To demonstrate sensitivity, we performed localization analysis on a mild air leak, which was achieved by removing staples to obtain a 15 ml/breath of air leak.

| Elimination of heartbeat sound via noise canceling
During air leak sound acquisition and analysis in clinical settings, confounding sound signals originating from background noise (e.g., equipment, other auscultative sounds) can contaminate the air leak sound signals. In particular, while the intensity of background noise can be reduced during measurements, sounds generated by the heart could interfere with measurements due to proximity to the lung.
To address this potential issue, we investigated if air leak sound could be detected and isolated in the presence of heart sounds ( Figure S12a and Video S9). We generated a sound mixture by adding air leak sound signals collected from swine with staple-line failure (Figure 3h) and heart sounds obtained from a healthy human ( Figure S12b-d).
Spectral analysis of the sounds revealed that frequency of human heart sounds remained below 400 Hz. This substantial difference in frequency range between heart sounds and pulmonary air leak sounds allowed us to effectively attenuate the heartbeat noise using highpass filtering to extract the air leak signal (Figure S13a-c and Video S10).

| DISCUSSION
Auscultation of heart and lungs, typically with a stethoscope, has been used for cardiopulmonary evaluation and diagnosis for over a century, 21 but remains reliant on the subjective experience of the care provider. 22 Leveraging the established principle that respiratory sounds can offer diagnostic insight, we developed an objective, quantitative sound analysis methodology that can discern distinct sound signatures of pulmonary air leaks based on key parameters such as frequency distribution and loudness level (Figure 1). We evaluated air leaks in both small and large animal models to demonstrate the high sensitivity and clinical relevance of this methodology, and determined: (a) air leaks generate discernable sounds that contain harmonic series, (b) air leak sound properties correlate with volume of air loss, and thus leak severity (Figures 2 and 3), and (c) air leak loudness correlates with  Figure 4). To maximize clinical relevance, we investigated pulmonary air leaks in a swine model due to the anatomical and respiratory (e.g., airway pressure, compliance, tidal volume) similarities to human lungs. 19 To emulate a realistic hospital setting with noisy environment, all studies were conducted in an operating room with standard medical equipment and background noises.
In pulmonary air leaks, air escapes the lung through a defect in a bronchial staple line or the visceral pleura due to a pressure gradient between the alveoli and thorax. If sufficient kinetic energy is transferred from the airflow to surrounding pleural tissue, the tissue oscillates at its resonance frequency which can generate audible sounds (similar to vocal cord vibration during phonation) ( Figure S2). Our results obtained from both small and large lungs consistently showed that sounds generated due to air leak were primarily dependent on the scale of the driving force (i.e., the pressure difference between inside and outside the lungs, ΔP) and the pleural defect geometry (size, shape), which together modulated the resulting sound loudness, pitch, and spectral density ( Figures S5-S10). We applied signal processing algorithms, including noise filtering, to isolate air leak sound signatures for analysis, as envisioned clinical use of this methodology would require filtration of ambient sounds from the patient's heart, medical equipment, and other background noise. 21 This study applied a high-pass filter (cutoff frequency, f c = 500 Hz) to attenuate recorded heart sounds for analysis of air leak in an open thoracotomy.
By tuning algorithm parameters such as attenuation rate and cutoff frequency of the filter, we detected distinct air leak sound frequency patterns and spectral density profiles, and confirmed that air leak sounds could be isolated from a sound mixture using a high-pass filtering algorithm (Figures S12 and S13). Additional advanced noise cancellation methods, such as active or adaptive noise canceling, could be implemented to improve signal-to-noise ratio. While band pass filters and spectral analysis have previously been applied to process heart and lung sounds, our methodology is the first designed to quantitate and characterize pulmonary air leak sounds. [23][24][25][26][27] Notably, in both rat and swine models, we discovered that air leak sounds contained harmonic series, in which each frequency was an integer multiple of the fundamental frequency of the oscillating pleural tissue (Figures S6 and S9). 18 To the best of our knowledge, this is the first report of harmonic series as a feature of pulmonary air leak.
We also determined a statistically significant inverse correlation between sound power and band frequency in both rat and swine ( Figure 2j, Figure 3j,k, and Figure S9c). The correlation between frequency and relative intensity of bands in air leak harmonic series, as well as the absolute value of the fundamental frequency, may correspond to physical and pathological features, such as leak location, geometry, underlying lung disease, and tissue stiffness. The fundamental frequency and relative intensity of bands in these harmonic series of air leak sounds may correspond to physical and pathological features, such as leak location, geometry, underlying lung disease, and tissue stiffness. Although the mechanisms of air leak resolution remain to be elucidated, healing likely depends on anatomical factors, as mechanical strain and ventilation are not uniformly distributed throughout the lung, 28,29 and also on patient-specific factors such as age, underlying lung disease, co-morbidities, immunocompetency, and need for positive-pressure ventilation. Air leak sound signatures and harmonics may correlate with these variables and be used to better understand and predict air leak healing in an individualized manner.
Prior to beginning this study, we interviewed 66 practicing healthcare providers who treat patients with pulmonary air leak (e.g., surgeons, pulmonologists, emergency medicine, nurses, etc.) to confirm the clinical need for improved methods of detecting, assessing, and treating pulmonary air leak ( Figure S14). This study was therefore motivated by the fact that existing modalities for detecting air leaks did not allow for precise assessment or localization, and established air leak scoring systems, which were based on nominal categories and analog graduations, were not widely used due to high inter-observer variability [30][31][32] (Tables S2 and S3). Methods for intraoperative assessment of air leaks rely on submerging the lung in water or exogenous surfactant (i.e., Yang's bubble solution) and visualizing air bubbles that form at the leak site. Results of these air leak tests can be difficult to assess, particularly when direct visualization is difficult, including in peripheral or posterior lung regions or during thoracoscopic [video-assisted thoracoscopic surgery (VATS)] procedures. 6,33 Additionally, these methods are qualitative and subjective (Table S4). Digital chest tube drainage systems attempt to quantitate air leaks, however, are limited by high cost and variable results. No current modality enables precise localization of air leak sites. Diagnostic modalities such as auscultation, X-ray, computed tomography (CT) scan, and ultrasound can only detect the presence of pneumothorax, and offer minimal or no information about air leak location or severity-a gap in diagnostic capabilities that significantly limits air leak treatment (Table S5). 6,[34][35][36][37][38][39][40] Here we demonstrate precise localization of air leak site within 1 cm. Finer resolution may be achievable with a smaller microphone or decreased step size in sound intensity matrix measurements. Existing adjunct interventions (e.g., applied suction, pleurodesis, polymer sealant) are inconsistently used due to the lack of objective metrics for leak assessment, difficulty predicting which patients may benefit, and inability to target delivered treatments to the injured region. As a result, clinical management of air leak is often conservative, relying on continued chest tube drainage of air and fluid until the leak heals on its own. 41,42 In comparison, the  Table S6). While the water submersion test is used as the standard method for intraoperative assessment of pulmonary air leak, it can be challenging to visualize peripheral or posterior lung regions, and many air leaks may be missed.
Our system overcomes this challenge because it can detect air flow from the leak without visualization of the leak site. Our method could be easily adapted for use in (b) VATS procedures. Standard thoracoscopic ports were 5-12 mm, and the microphone used in this study was 5 mm in diameter. Thus, a smaller microphone may be required to accommodate all surgical instruments that need to pass through the port. The application in VATS is particularly compelling as methods for detecting pulmonary air leak intraoperatively, such as the water submersion test or Yang's bubble solution test, are challenging to perform during VATS procedures. For lung cancer resection in the United States alone, an estimated 66,000 patients undergo open lung surgery each year and an additional 54,000 undergo VATS procedures. 44 Therefore, a significant patient population could benefit from these applications.
The ability to apply quantitative sound analysis in a non-invasive manner through a closed chest presents the opportunity to benefit the greatest number of patients. Globally, 20 million patients receive positive pressure mechanical ventilation each year, and are at increased risk for developing pulmonary air leaks due to barotrauma and underlying lung disease. 45 As pulmonary air leak can rapidly progress to a potentially fatal tension pneumothorax, rapid detection of air leak is imperative ( Figure S1). We envision a modified configuration of our sound analysis system in which wearable sensors are placed on the chest to continuously monitor pulmonary sounds to rapidly detect pulmonary air leak. We recognize several challenges toward application of our system in a closed chest due to transmission characteristics of the intervening chest wall, including differential coupling, sound attenuation, and spectral distortion. To address these challenges and enable accurate, non-invasive analysis of air leak sounds, we propose incorporation of: (a) accelerometer contact microphone array on the chest, (b) high pass filter and adaptive filtering, and (c) triangulation for accurate localization ( Figure S15). While the magnitude of the sound signal would be lower in the closed chest, prior work has demonstrated the utility of contact accelerometers for precise, non-invasive analysis of cardiopulmonary sounds of low magnitudes, and could be extended to use in our system. [46][47][48] Whereas air coupled microphones are highly sensitive to environmental noises, the incorporation of multiple accelerometers onto the chest wall could enable the use of signal processing techniques including decomposition, independent components analysis, and modulation filtering to allow for precise analysis of sound signals originating from air leak.

| CONCLUSIONS
The goal of this proof-of-concept study was to demonstrate that pulmonary air leaks generate unique, quantifiable sound signals. The ability of this method to precisely assess and localize pulmonary air leaks through non-invasive acoustic signal analysis through the chest wall remains to be studied. Future device iterations will focus on modifying this system and methodology for use in a closed chest. The ability to precisely locate air leaks in a real-time and minimally invasive manner could enable locally targeted and patient-specific administration of therapeutics, such as a polymer-based lung sealant to expedite air leak treatment and improve outcomes. Similar sound detection and analysis methodologies could also be developed for diagnostic evaluations of other organs, such as heart and intestine, where sounds can be evaluated to quantify injury or disease states.

| MATERIALS AND METHODS
Detailed methods can be found in Supporting Information. Briefly, the sound acquisition and analysis system comprised a miniature microphone (diameter: 5.6 mm; length: 3 mm), motorized stage to position the microphone, signal amplifier, audio interface, and custom-written MATLAB codes (Figure 1a and Figure S3a,b). Notably, standard thoracoscopic ports had inner diameters up to 12 mm, which could allow insertion of the mini-microphone for minimally invasive sound analysis in situ. Sound signals obtained with the microphone passed through the amplifier and audio interface to the computer for sound processing and quantification using custom-written MATLAB scripts, which removed ambient noise, amplified signals, and quantified acquired sounds to predict severity and determine air leak location Sound signal originating from rat and swine pulmonary air leaks were recorded with Sony ECM 77B and analyzed using custom-written MATLAB codes. All animal procedures were approved by and conducted in accordance with the Institutional Animal Care and Use Committee of Stevens Institute of Technology and Columbia University.